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2006

Active learning in very large databases

9 years 2 months ago
Active learning in very large databases
Abstract. Query-by-example and query-by-keyword both suffer from the problem of "aliasing," meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.
Navneet Panda, Kingshy Goh, Edward Y. Chang
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where MTA
Authors Navneet Panda, Kingshy Goh, Edward Y. Chang
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